#
T-GATE

T-GATE accelerates inference for Stable Diffusion, PixArt, and Latency Consistency Model pipelines by skipping the cross-attention calculation once it converges. This method doesn’t require any additional training and it can speed up inference from 10-50%. T-GATE is also compatible with other optimization methods like DeepCache.

Before you begin, make sure you install T-GATE.

    pip install tgate
    pip install -U torch diffusers transformers accelerate DeepCache

To use T-GATE with a pipeline, you need to use its corresponding loader.

Pipeline | T-GATE Loader
--- | ---
PixArt | TgatePixArtLoader
Stable Diffusion XL | TgateSDXLLoader
Stable Diffusion XL + DeepCache | TgateSDXLDeepCacheLoader
Stable Diffusion | TgateSDLoader
Stable Diffusion + DeepCache | TgateSDDeepCacheLoader

Next, create a `TgateLoader` with a pipeline, the gate step (the time step to stop calculating the cross attention), and the number of inference steps. Then call the `tgate` method on the pipeline with a prompt, gate step, and the number of inference steps.

Let’s see how to enable this for several different pipelines.

PixArt
Stable Diffusion XL
StableDiffusionXL with DeepCache
Latent Consistency Model

Accelerate `PixArtAlphaPipeline` with T-GATE:

    import torch
    from diffusers import PixArtAlphaPipeline
    from tgate import TgatePixArtLoader

    pipe = PixArtAlphaPipeline.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", torch_dtype=torch.float16)

    gate_step = 8
    inference_step = 25
    pipe = TgatePixArtLoader(
           pipe,
           gate_step=gate_step,
           num_inference_steps=inference_step,
    ).to("cuda")

    image = pipe.tgate(
           "An alpaca made of colorful building blocks, cyberpunk.",
           gate_step=gate_step,
           num_inference_steps=inference_step,
    ).images[0]

T-GATE also supports StableDiffusionPipeline and PixArt-alpha/PixArt-LCM-XL-2-1024-MS.

##
Benchmarks

Model | MACs | Param | Latency | Zero-shot 10K-FID on MS-COCO
--- | --- | --- | --- | ---
SD-1.5 | 16.938T | 859.520M | 7.032s | 23.927
SD-1.5 w/ T-GATE | 9.875T | 815.557M | 4.313s | 20.789
SD-2.1 | 38.041T | 865.785M | 16.121s | 22.609
SD-2.1 w/ T-GATE | 22.208T | 815.433 M | 9.878s | 19.940
SD-XL | 149.438T | 2.570B | 53.187s | 24.628
SD-XL w/ T-GATE | 84.438T | 2.024B | 27.932s | 22.738
Pixart-Alpha | 107.031T | 611.350M | 61.502s | 38.669
Pixart-Alpha w/ T-GATE | 65.318T | 462.585M | 37.867s | 35.825
DeepCache (SD-XL) | 57.888T | - | 19.931s | 23.755
DeepCache w/ T-GATE | 43.868T | - | 14.666s | 23.999
LCM (SD-XL) | 11.955T | 2.570B | 3.805s | 25.044
LCM w/ T-GATE | 11.171T | 2.024B | 3.533s | 25.028
LCM (Pixart-Alpha) | 8.563T | 611.350M | 4.733s | 36.086
LCM w/ T-GATE | 7.623T | 462.585M | 4.543s | 37.048

The latency is tested on an NVIDIA 1080TI, MACs and Params are calculated with calflops, and the FID is calculated with PytorchFID.

Update on GitHub